Neural network classification of late gamma band EEG features

It has been a while since my last post. I guess job hunting has an higher priority, at the moment but, today, I was able to go through another paper. Today I read “Neural network classification of late game band electroencephalogram features” from Ravi, K V R, and Ramaswamy Palaniappan (2006).

I’ve always been fascinated with artificial intelligence and, especially, the way we try to recreate the human brain neural network. In this study, Neural Networks are used to classify individuals, much like current biometric sensors, iris or face recognition systems or vocal/sound recognition techniques.

It’s a different study from the previous ones I’ve read. I enjoyed reading how EEG data, after much development, is able to provide features that can be used to classify and identify individuals. The techniques used in this study are worth future study from my side, like the Principal Component Analysis (PCA), the Butterworth forth and reverse filtering, or the Simplified Fuzzy ARTMAP (SFA) classification algorithm.

It was nice to understand how new approaches stand against old approaches, like in this study, where the authors concluded that Back-propagation (BP) algorithm prevailed a better method than SFA. Nonetheless, it’s also true that development on specific parts (preprocessing, signal processing, data analysis) can be enhanced and, perhaps, change the outcomes of future experiments.

On a similar approach, I’d like to make one or two readings on Genetic Neural Networks.